With the popularity of different types of sensors embedded in vehicles, traffic sign recognition (TSR) has been receiving more and more interest in recent years. TSR provides semantic information of different traffic signs, such as “STOP” and “Slow Down”, which are essentially important for the assistive or autonomous driving of a vehicle.
Current traffic sign detection systems have achieved much progress through improving either the Viola-Jones approach or Histogram of Gradients (HoG) based approach. Further, most techniques use the binary support vector machine (SVM) to train the perfect samples (also referred to as ground truth rectangles in the image) into a detector to learn an optimal hyperplane to separate the positive and negative traffic sign samples.
There is a need to improve the accuracy for traffic sign detection and recognition. There is also a need to improve the speed of traffic sign detection and recognition for real-time applications.